Last updated: 2021-02-19

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Introduction

Following sequencing and obtaining .fastq.gz file, the first step is to perform trimming and mapping of the sequencing data to generate bam files. All these steps were performed using bash code.

Bam files were then used for removal of duplicated and low quality (<Q30) reads and subsequently subjected to read counting to generate a count matrix.

Human developmental bulk ATAC-seq were performed using paired-end sequencing method and below are the scripts for primary processing of paired-end sequencing read.

Used libraries and functions

skewer/0.2.2
bwa/0.7.17
samtools/1.8
parallel
subread/1.5.0
bedtools/2.27.1
macs14
bedops/2.4.20

1. Basic processing of all samples

Trimming of sequencing read

#!/bin/bash

# function to run skewer quality trimming
runskew(){
FQZ1=$1
FQZ2=`echo $FQZ1 | sed 's/_R1.fastq.gz/_R2.fastq.gz/'`
skewer -t 8 -q 20 $FQZ1 $FQZ2
}
export -f runskew

# actually run skewer
parallel -j3 runskew ::: *_R1.fastq.gz

Mapping of Skewer trimmed .fastq to mouse reference genome using BWA

runbwamempe() {
FQ1=$1
FQ2=`echo $FQ1 | sed 's/R1.fastq-trimmed-pair1.fastq/R1.fastq-trimmed-pair2.fastq/'`
BASE=`echo $FQ1 | sed 's/_R1.fastq-trimmed-pair1.fastq//'`

REF=/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/refgenome/Homo_sapiens.GRCh38.dna_sm.primary_assembly.fa

bwa mem -t 20 $REF $FQ1 $FQ2 \
| samtools view -uSh - \
| samtools sort -@10 -o ${BASE}.sort.bam
samtools index ${BASE}.sort.bam

samtools flagstat ${BASE}.sort.bam > ${BASE}.sort.bam.stats
}
export -f runbwamempe


# actually run bwa pe
ls *_R1.fastq-trimmed-pair1.fastq | parallel -u -j4 runbwamempe {}

Remove duplicated reads

#!/bin/bash

nodup(){
BAM=$1
OUT=`echo $BAM | sed 's/.bam/_nodup.bam/'`
samtools rmdup $BAM $OUT
}
export -f nodup
parallel nodup ::: `ls *bam | grep -v dup`

Merge replicated .bam files.

Make a directory called “merged” and ln all .bam files to the folder and perform the following.

#!/bin/bash

samtools view -H `ls *bam | head -1` > header.sam
for BASE in `ls *bam | cut -d '_' -f1 | sort -u ` ; do
  rm $BASE.mg.bam
  samtools merge -h header.sam $BASE.mg.bam *${BASE}*bam &
done
wait

ls: cannot access *bam: No such file or directory
bash: line 2: samtools: command not found
ls: cannot access *bam: No such file or directory

Remove low quality (Q<30) sequencing reads

#!/bin/bash

# function to run bwa in paired end mode
runsamtoolsmapq() {
BAM=$1

NAME=`echo ${BAM} | sed 's/.bam//'`
REF=/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/refgenome/Homo_sapiens.GRCh38.dna_sm.primary_assembly.fa

samtools view -q 30 -f 0x2 -b -h ${BAM} > ${NAME}.mapq30.bam

wait

samtools index ${NAME}.mapq30.bam
samtools flagstat ${BAM}.mapq30.bam > ${BAM}.mapq30.bam.flagstats
samtools idxstats ${BAM}.mapq30.bam > ${BAM}.mapq30.bam.idxstats

}
export -f runsamtoolsmapq


# actually run runsamtoolsmapq pe 
ls *.bam | parallel -u -j5 runsamtoolsmapq {}

Investigate the distribution of reads on different chromosome

#!/bin/bash

cntrds(){
BAM=$1
samtools view -q30 $BAM | cut -f3 | sort -T .| uniq -c | sed "s/^/${BAM}/"
}
export -f cntrds

ls *bam | parallel cntrds {} | tee -a read_counts2_q30.txt

2. 1kbp +/- TSS

Counting reads from bam files across human reference genome for 1kbp +/- TSS to identify associated genes

#!/bin/bash

SAF=/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/refgenome/tss_1kbp.saf
OUTPE=atac_hum_tss_pe_mapk30_q30.mx

#featureCounts -p -Q 30 -T 20 -s 0 -a $SAF -F SAF -o $OUTPE *bam

Tidy 1kbp +/- TSS count matrix


#!/bin/bash

for MX in `ls *mx` ; do
   sed 1d $MX | sed 's/.mg.mapq30.bam//g' > $MX.all
   sed 1d $MX | cut -f1-6 | sed 's/.mg.mapq30.bam//g' > $MX.chr
   sed 1d $MX | cut -f1,7- | sed 's/.mg.mapq30.bam//g' > $MX.all.fix
   sed 1d $MX | cut -f1,7-26 | sed 's/.mg.mapq30.bam//g' > $MX.hum.fix
   
done
wait

Filter out low counts genes from 1kbp +/- TSS count matrix

Filtering out low counts genes by running the following filter.sh as

bash filter.sh hrna_dev_mf_fulllen_se_strrev_q30.mx.all.fix

filter.sh

head -1 $1 > ${1}_filt
awk '{
  min = max = sum = $2;       # Initialize to the first value (2nd field)
  sum2 = $2 * $2              # Running sum of squares
  for (n=3; n <= NF; n++) {   # Process each value on the line
    if ($n < min) min = $n    # Current minimum
    if ($n > max) max = $n    # Current maximum
    sum += $n;                # Running sum of values
    sum2 += $n * $n           # Running sum of squares
  }
  print sum/(NF-1) ;
}' $1 > avg
paste avg $1 | awk '$1 >= 10' | cut -f2- | tr ' ' '\t' >> ${1}_filt
rm avg

3. Peaks

Peak call of individual sample

#!/bin/bash
BAMS='*bam'
BASENAME=humanATAC
PEAKBED=${BASENAME}_peaks.bed
PEAKSAF=${BASENAME}_peaks.saf
OUT=${BASENAME}_pks.txt
MX=${BASENAME}_pks_se.mx

PATH=$PATH:/usr/local/installed/macs/1.4.2-1/python-2.7.11/.//bin/

ls $BAMS | parallel macs14 -t {} -n {}_macs

done

exit

Curate peaks that exist in more than 2 or 3 samples to form a peak set

for BED in *peaks.bed ; do
 awk '{OFS="\t"} {if ($2<1) print $1,1,$3 ; else print $0 }' $BED | awk 'NF=="5"'> tmp
 mv tmp $BED
done

rm humanATAC_peaks_cov*.bed
for COV in 2 3 ; do
  bedtools multiinter -i *_macs_peaks.bed \
 | cut -f-4 | awk -v C=$COV '$4>=C && NF==4' \
 | bedtools merge -i - > humanATAC_peaks_cov${COV}.bed
done

exit

Remove blacklisted peaks

#!/bin/bash

wget https://github.com/Boyle-Lab/Blacklist/blob/master/lists/Blacklist_v1/hg38-blacklist.bed.gz

cat hg38.blacklist.bed | sed 's/chr//' > hg38.blacklist.tidy.bed

BL=/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/refgenome/hg38.blacklist.tidy.bed

bedops -n -1 humanATAC_peaks_cov2.bed $BL  > humanATAC_peaks_cov2_rmBL.bed

bedops -n -1 humanATAC_peaks_cov3.bed $BL  > humanATAC_peaks_cov3_rmBL.bed

Count individual sample to the curated peak set

#!/bin/bash

for BED in humanATAC*bed ; do

  SAF=$BED.saf
  OUT=$SAF.pe.q30.mx
  awk '{OFS="\t"} {print "PK"NR"_"$1":"$2"-"$3,$1,$2,$3,"+"}' $BED > $SAF
  ( featureCounts -p -Q 30 -T 20 -s 0 -a $SAF -F SAF -o $OUT *bam
  sed 1d $OUT | cut -f1,7- > tmp ; mv tmp $OUT ) &

done

awk: fatal: cannot open file `humanATAC*bed' for reading (No such file or directory)
bash: line 7: featureCounts: command not found

Tidy peak count matrix

#!/bin/bash

for MX in `ls *mx` ; do
   cat $MX | sed 's/.mg.mapq30.bam//g'  > $MX.fix
   cat $MX | cut -f1-21 | sed 's/.mg.mapq30.bam//g' > $MX.hum.fix
done
wait

Filter out low counts genes from peak count matrix